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Found 43 papers, 7 papers with code
Assortment Planning with Sponsored Products
In the rapidly evolving landscape of retail, assortment planning plays a crucial role in determining the success of a business.
Non-monotone Sequential Submodular Maximization
In this paper, we study a fundamental problem in submodular optimization, which is called sequential submodular maximization.
TableGPT: Towards Unifying Tables, Nature Language and Commands into One GPT
Tables are prevalent in real-world databases, requiring significant time and effort for humans to analyze and manipulate.
Beyond Submodularity: A Unified Framework of Randomized Set Selection with Group Fairness Constraints
Our problem involves a global utility function and a set of group utility functions for each group, here a group refers to a group of individuals (e. g., people) sharing the same attributes (e. g., gender).
Achieving Long-term Fairness in Submodular Maximization through Randomization
Unlike previous studies in this area, we allow for randomized solutions, with the objective being to calculate a distribution over feasible sets such that the expected number of items selected from each group is subject to constraints in the form of upper and lower thresholds, ensuring that the representation of each group remains balanced in the long term.
Group Fairness in Non-monotone Submodular Maximization
Our goal is to select a set of items that maximizes a non-monotone submodular function, while ensuring that the number of selected items from each group is proportionate to its size, to the extent specified by the decision maker.
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning.
Worst-Case Adaptive Submodular Cover
We also study a worst-case maximum-coverage problem, a dual problem of the minimum-cost-cover problem, whose goal is to select a group of items to maximize its worst-case utility subject to a budget constraint.
Magnetic Resonance Fingerprinting with compressed sensing and distance metric learning
Adaptively learning a distance metric from the undersampled training data can significantly improve the matching accuracy of the query fingerprints.
Streaming Adaptive Submodular Maximization
Many sequential decision making problems can be formulated as an adaptive submodular maximization problem.
Partial-Monotone Adaptive Submodular Maximization
We show that a sampling-based policy achieves an approximation ratio of $(m+1)/10$ if the utility function is $m$-adaptive monotone and adaptive submodular.
Group Equality in Adaptive Submodular Maximization
In this paper, we study the classic submodular maximization problem subject to a group equality constraint under both non-adaptive and adaptive settings.
GRU-TV: Time- and velocity-aware GRU for patient representation on multivariate clinical time-series data
In this study, we propose a time- and velocity-aware gated recurrent unit model (GRU-TV) for patient representation learning of clinical multivariate time-series data in a time-continuous manner.
Partial-Adaptive Submodular Maximization
Although this approach can take full advantage of feedback from the past to make informed decisions, it may take a longer time to complete the selection process as compared with the non-adaptive solution where all selections are made in advance before any observations take place.
Submodular Optimization Beyond Nonnegativity: Adaptive Seed Selection in Incentivized Social Advertising
We formulate this problem as a seed selection problem whose objective function is non-monotone and it might take on negative values, making existing results on submodular optimization and influence maximization not applicable to our setting.
Optimal Sampling Gaps for Adaptive Submodular Maximization
Although the benefit of running machine learning algorithms on the reduced data set is obvious, one major concern is that the performance of the solution obtained from samples might be much worse than that of the optimal solution when using the full data set.
Adaptive Regularized Submodular Maximization
For the case when $g$ is adaptive monotone and adaptive submodular, we develop an effective policy $\pi^l$ such that $g_{avg}(\pi^l) - c_{avg}(\pi^l) \geq (1-\frac{1}{e}-\epsilon)g_{avg}(\pi^o) - c_{avg}(\pi^o)$, using only $O(n\epsilon^{-2}\log \epsilon^{-1})$ value oracle queries.
A Random Algorithm for Profit Maximization with Multiple Adoptions in Online Social Networks
Through "word of mouth" effects, information or product adoption could spread from some influential individuals to millions of users in social networks.
Social and Information Networks
To Learn Effective Features: Understanding the Task-Specific Adaptation of MAML
Model Agnostic Meta-Learning~(MAML)~(\cite{finn2017model}) is one of the most well-known gradient-based meta learning algorithms, that learns the meta-initialization through the inner and outer optimization loop.
Adaptive Submodular Meta-Learning
Our objective is to adaptively select a group of items that achieve the best performance over a set of tasks, where each task is represented as an adaptive submodular function that maps sets of items and their states to a real number.
MRPB 1.0: A Unified Benchmark for the Evaluation of Mobile Robot Local Planning Approaches
Local planning is one of the key technologies for mobile robots to achieve full autonomy and has been widely investigated.
Robotics
Adaptive Cascade Submodular Maximization
The input of our problem is a set of items, each item is in a particular state (i. e., the marginal contribution of an item) which is drawn from a known probability distribution.
A deep belief network-based method to identify proteomic risk markers for Alzheimer disease
Besides identifying a proteomic risk marker and further reinforce the link between metabolic risk factors and Alzheimer disease, this paper also suggests that apidonectin-linked pathways are a possible therapeutic drug target.
Assortment Optimization with Repeated Exposures and Product-dependent Patience Cost
After browsing all products in one stage, if the utility of a product exceeds the utility of the outside option, the consumer proceeds to purchase the product and leave the platform.
Laplacian pyramid-based complex neural network learning for fast MR imaging
A Laplacian pyramid-based complex neural network, CLP-Net, is proposed to reconstruct high-quality magnetic resonance images from undersampled k-space data.
Variational Fair Clustering
We derive a general tight upper bound based on a concave-convex decomposition of our fairness term, its Lipschitz-gradient property and the Pinsker's inequality.
Adaptive Robust Optimization with Nearly Submodular Structure
Then we propose a approximate solution to this problem when all reward functions are submodular.
Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions
While sub-optimality is not guaranteed for non-convex problems, this result shows that log-barrier extensions are a principled way to approximate Lagrangian optimization for constrained CNNs via implicit dual variables.
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz
Note that under the our model, the probability of a question being answered depends on the location of that question, as well as the set of other questions placed ahead of that question, this makes our problem fundamentally different from existing studies on submodular optimization.
Variational Community Partition with Novel Network Structure Centrality Prior
In this paper, we proposed a novel two-stage optimization method for network community partition, which is based on inherent network structure information.
Clustering
Combinatorial Optimization
+2
Social and Information Networks
Physics and Society
Modern Convex Optimization to Medical Image Analysis
Many researchers and companies have invested significant efforts in the developments of advanced medical image analysis methods; especially in the two core studies of medical image segmentation and registration, segmentations of organs and lesions are used to quantify volumes and shapes used in diagnosis and monitoring treatment; registration of multimodality images of organs improves detection, diagnosis and staging of diseases as well as image-guided surgery and therapy, registration of images obtained from the same modality are used to monitor progression of therapy.
Multi-region segmentation of bladder cancer structures in MRI with progressive dilated convolutional networks
Precise segmentation of bladder walls and tumor regions is an essential step towards non-invasive identification of tumor stage and grade, which is critical for treatment decision and prognosis of patients with bladder cancer (BC).
HyperDense-Net: A hyper-densely connected CNN for multi-modal image segmentation
Therefore, the proposed network has total freedom to learn more complex combinations between the modalities, within and in-between all the levels of abstraction, which increases significantly the learning representation.
Ranked #1 on
Medical Image Segmentation
on iSEG 2017 Challenge
Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation
We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.
Ranked #1 on
Infant Brain Mri Segmentation
on iSEG 2017 Challenge
Isointense Infant Brain Segmentation with a Hyper-dense Connected Convolutional Neural Network
Neonatal brain segmentation in magnetic resonance (MR) is a challenging problem due to poor image quality and low contrast between white and gray matter regions.
No Time to Observe: Adaptive Influence Maximization with Partial Feedback
In the full-feedback model, we select one seed at a time and wait until the diffusion completes, before selecting the next seed.
Social and Information Networks
Shape Complexes in Continuous Max-Flow Hierarchical Multi-Labeling Problems
Although topological considerations amongst multiple labels have been previously investigated in the context of continuous max-flow image segmentation, similar investigations have yet to be made about shape considerations in a general and extendable manner.
A Proximal Bregman Projection Approach to Continuous Max-Flow Problems Using Entropic Distances
One issue limiting the adaption of large-scale multi-region segmentation is the sometimes prohibitive memory requirements.
A Continuous Max-Flow Approach to Multi-Labeling Problems under Arbitrary Region Regularization
The incorporation of region regularization into max-flow segmentation has traditionally focused on ordering and part-whole relationships.
RANCOR: Non-Linear Image Registration with Total Variation Regularization
Optimization techniques have been widely used in deformable registration, allowing for the incorporation of similarity metrics with regularization mechanisms.
A Continuous Max-Flow Approach to General Hierarchical Multi-Labeling Problems
Multi-region segmentation algorithms often have the onus of incorporating complex anatomical knowledge representing spatial or geometric relationships between objects, and general-purpose methods of addressing this knowledge in an optimization-based manner have thus been lacking.
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry
Segmenting 3D endfiring transrectal ultrasound (TRUS) prostate images efficiently and accurately is of utmost importance for the planning and guiding 3D TRUS guided prostate biopsy.
T-Drive: Driving Directions Based on Taxi Trajectories
GPS-equipped taxis can be regarded as mobile sensors probing traffic flows on road surfaces, and taxi drivers are usually experienced in finding the fastest (quickest) route to a destination based on their knowledge.
